/*
 * Hivemall: Hive scalable Machine Learning Library
 *
 * Copyright (C) 2015 Makoto YUI
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
/*
 * Copyright (c) 2010 Haifeng Li
 *   
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *  
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package hivemall.smile.classification;

import smile.classification.Classifier;
import smile.data.Attribute;

/**
 * Abstract classifier trainer.
 * 
 * @param <T>
 *            the type of input object.
 * 
 * @author Haifeng Li
 * @author Makoto Yui
 */
public abstract class ClassifierTrainer<T> {
    /**
     * The feature attributes. This is optional since most classifiers can only
     * work on real-valued attributes.
     */
    protected Attribute[] attributes;

    /**
     * Constructor.
     */
    public ClassifierTrainer() {}

    /**
     * Constructor.
     * 
     * @param attributes
     *            the attributes of independent variable.
     */
    public ClassifierTrainer(Attribute[] attributes) {
        this.attributes = attributes;
    }

    /**
     * Sets feature attributes. This is optional since most classifiers can only
     * work on real-valued attributes.
     * 
     * @param attributes
     *            the feature attributes.
     */
    public void setAttributes(Attribute[] attributes) {
        this.attributes = attributes;
    }

    /**
     * Learns a classifier with given training data.
     * 
     * @param x
     *            the training instances.
     * @param y
     *            the training labels.
     * @return a trained classifier.
     */
    public abstract Classifier<T> train(T[] x, int[] y);
}
